tooluniverse-metabolomics-analysis

Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomarkers, performing flux balance analysis, or characterizing metabolic phenotypes in disease, drug response, or physiological conditions.

Quality

78%

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Securityby

Low

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tessl review fix ./skills/bio/tooluniverse-metabolomics-analysis/SKILL.md

Quality

Content

57%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body provides a well-sequenced, mostly executable metabolomics pipeline with strong section structure, but it is monolithic and padded, inlining content that belongs in separate reference files and lacking explicit validation feedback loops for batch operations.

Suggestions

Split the ~110-line mock report template and the per-phase code into one-level-deep reference files (e.g. REPORT_TEMPLATE.md, PHASE_CODE.md) and link them from SKILL.md to improve progressive disclosure.

Add explicit validation/verification checkpoints to the workflow (e.g. after QC and normalization: check CV thresholds, flag samples failing QC, and retry before proceeding to differential analysis).

Replace undefined helper stubs (find_metabolite_enzymes, calculate_confidence, calculate_pathway_dysregulation) with concrete implementations or document their expected interface so the code is fully executable.

Dimension	Reasoning	Score
Conciseness	Mostly actionable code but noticeably padded in places — a ~110-line mock report template and verbose restating docstrings ('More robust than TIC to large metabolite changes') could be tightened.	3 / 5
Actionability	Most phases have concrete executable Python, but a few rely on undefined helpers (find_metabolite_enzymes, calculate_confidence, calculate_pathway_dysregulation) or pass stubs (pathway_topology_analysis, integrate_omics_pathway).	4 / 5
Workflow Clarity	Clear 8-phase sequence with a workflow diagram, but no explicit validate-fix-retry feedback loops for this batch analytical pipeline, capping the score at 3 per the batch-operation rule.	3 / 5
Progressive Disclosure	Good section headers give structure, but all detail (full report template, every code example) is inlined in a ~760-line monolithic SKILL.md with no bundle files or one-level-deep references to split it out.	3 / 5
	Total	13 / 20 Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is comprehensive, specific, and clearly distinguishes the skill's metabolomics niche with concrete trigger phrases in third-person voice. It explicitly answers both what the skill does and when to use it.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('metabolite identification, quantification, pathway analysis, and metabolic flux' plus 'normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery') with comprehensive coverage.	5 / 5
Completeness	Explicitly answers both what (identification, quantification, normalization, pathway analysis) and when ('Use when analyzing metabolomics datasets, identifying differential metabolites... or characterizing metabolic phenotypes').	5 / 5
Trigger Term Quality	Covers natural user terms including platform names (LC-MS, GC-MS, NMR) and task phrases ('differential metabolites', 'flux balance analysis', 'metabolic biomarkers', 'metabolic pathways'); only minor synonyms/extensions absent.	5 / 5
Distinctiveness Conflict Risk	Clear metabolomics niche with distinct triggers (LC-MS/GC-MS/NMR, flux balance analysis) and minimal overlap with related omics skills, which are named only as integration partners.	5 / 5
	Total	20 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (765 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: wu-yc/LabClaw
Path: skills/bio/tooluniverse-metabolomics-analysis/SKILL.md
Commit: df37802

Reviewed: 8 hours ago

Table of Contents

Discovery Implementation Validation

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